The invention relates to magnetic resonance imaging (MRI) and image processing methods. More particularly, the invention relates to segmenting brain images into various structures found within the brain.
It is well known to employ MRI for visualization of internal anatomical structures such as brain imaging. Brain imaging, and particularly for use in brain volume measurement is of particular interest for studying degenerative brain diseases such as Alzheimer's disease (AD). Examination of the Alzheimer brain pathology shows extensive β-amyloid plaque, neuron tangles and brain atrophy. Typically, magnetic resonance imaging brain volume measurements are used to monitor the disease progression. Normal aging brain atrophy is only about a 3.5% decrease per decade, but the rate of atrophy increases in subjects exhibiting dementia. Thus, brain volume measurements provide a measurable correlation available to predict Alzheimer's disease. The diagnosis of other brain diseases such as tumors and edema also rely on brain volume measurement.
Segmentation techniques typically require some user interaction such as placing a seed point within an area of interest in order to initiate segmentation or registration processing. Further, in order to segment multiple structure or multiple tissue types within the brain generally requires repeating the seed placement and subsequent processing for the selected structure or tissue type. Thus, measuring brain volume from magnetic resonance images either by registration methods or by segmentation methods is typically tedious because there is considerable manual editing and processing needed.
Measurement of brain structures could lead to important diagnostic information and could also indicate the success or failure of a certain pharmaceutical drug. A necessary step to quantify brain structures is the availability of a segmentation technique of some sort. Other techniques, such as neural network techniques require a training stage for the algorithm. These techniques are time consuming, which make them less clinically desirable. Therefore, there is a need for a fast and accurate unsupervised segmentation technique.
What is needed is a method and system for segmenting medical images in an automatic manner with minimal user intervention. What is further needed is a method for segmenting medical images for a plurality of structures within an anatomical body of interest.